PSO based linear parameter varying-model predictive control for trajectory tracking of autonomous vehicles

Abstract

In this paper, Linear Parameter Varying-Model Predictive Control (LPV-MPC) for trajectory tracking for Autonomous Vehicles (AVs) is proposed. This method is based on the time-varying LPV is the form of the state space representation from the mathematical model of the vehicle. The LPV representation form which uses the dynamic model of the vehicle allows the incorporation of time-varying dynamics, providing a more accurate representation of the vehicle's behavior. The designed LPV-MPC controller for AVs is specifically designed to handle constraints in trajectory tracking. To enhance its performance, Particle Swarm Optimization (PSO) is employed as an optimization technique. PSO is used to tune the weighting matrices of the control parameters, optimizing the system response and improving trajectory tracking performance. To evaluate the effectiveness of the LPV-MPC system, extensive simulations are conducted and results are compared with Linear and Non-Linear MPCs. The main benefit of using the LPV-MPC method is its ability to calculate solutions almost as good as the non-linear MPC version yet significantly reducing the computational cost. The capability of the LPV-MPC controller as compared to the linear version is in its effective tracking, particularly for the non-linear reference trajectories.